Process-induced cell cycle oscillations in CHO cultures: Online monitoring and model-based investigation

The influence of process strategies on the dynamics of cell population heterogeneities in mammalian cell culture is still not well understood. We recently found that the progression of cells through the cell cycle causes metabolic regulations with variable productivities in antibody-producing Chimese hamster ovary (CHO) cells. On the other hand, it is so far unknown how bulk cultivation conditions, for example, variable nutrient concentrations depending on process strategies, can influence cell cycle-derived population dynamics. In this study, process-induced cell cycle synchronization was assessed in repeated-batch and fed-batch cultures. An automated flow cytometry set-up was developed to measure the cell cycle distribution online, using antibody-producing CHO DP-12 cells transduced with the cell cycle-specific fluorescent ubiquitination-based cell cycle indicator (FUCCI) system. On the basis of the population-resolved model, feeding-induced partial self-synchronization was predicted and the results were evaluated experimentally. In the repeated-batch culture, stable cell cycle oscillations were confirmed with an oscillating G1 phase distribution between 41% and 72%. Furthermore, oscillations of the cell cycle distribution were simulated and determined in a (bolus) fed-batch process with up to cells/ml. The cell cycle synchronization arose with pulse feeding only and ceased with continuous feeding. Both simulated and observed oscillations occurred at higher frequencies than those observable based on regular (e.g., daily) sample analysis, thus demonstrating the need for high-frequency online cell cycle analysis. In summary, we showed how experimental methods combined with simulations enable the improved assessment of the effects of process strategies on the dynamics of cell cycle-dependent population heterogeneities. This provides a novel approach to understand cell cycle regulations, control cell population dynamics, avoid inadvertently induced oscillations of cell cycle distributions and thus to improve process stability and efficiency.     

Nonequilibrium Population Dynamics of Phenotype Conversion of Cancer Cells

Tumorigenesis is a dynamic biological process that involves distinct cancer cell subpopulations proliferating at different rates and interconverting between them. In this paper we proposed a mathematical framework of population dynamics that considers both distinctive growth rates and intercellular transitions between cancer cell populations. Our mathematical framework showed that both growth and transition influence the ratio of cancer cell subpopulations but the latter is more significant. We derived the condition that different cancer cell types can maintain distinctive subpopulations and we also explain why there always exists a stable fixed ratio after cell sorting based on putative surface markers. The cell fraction ratio can be shifted by changing either the growth rates of the subpopulations (Darwinism selection) or by environment-instructed transitions (Lamarckism induction). This insight can help us to understand the dynamics of the heterogeneity of cancer cells and lead us to new strategies to overcome cancer drug resistance.     

Dynamics of yeast metabolism and regulation

The dynamic behavior of yeasts is discussed on the basis of transient experiments, such as pulses and shifts in continuous culture, and of oscillating synchronized cultures. The minimal elements of a structured model are evaluated and extensions by regulatory mechanisms are proposed. Segregation of a population in cell classes reflecting the position of a cell in the cell cycle, its individual age, is shown to be necessary in order to account for the spontaneous synchronization of continuous S. cerevisisae cultures.     

Tumor-Specific Chromosome Mis-Segregation Controls Cancer Plasticity by Maintaining Tumor Heterogeneity

Aneuploidy with chromosome instability is a cancer hallmark. We studied chromosome 7 (Chr7) copy number variation (CNV) in gliomas and in primary cultures derived from them. We found tumor heterogeneity with cells having Chr7-CNV commonly occurs in gliomas, with a higher percentage of cells in high-grade gliomas carrying more than 2 copies of Chr7, as compared to low-grade gliomas. Interestingly, all Chr7-aneuploid cell types in the parental culture of established glioma cell lines reappeared in single-cell-derived subcultures. We then characterized the biology of three syngeneic glioma cultures dominated by different Chr7-aneuploid cell types. We found phenotypic divergence for cells following Chr7 mis-segregation, which benefited overall tumor growth in vitro and in vivo. Mathematical modeling suggested the involvement of chromosome instability and interactions among cell subpopulations in restoring the optimal equilibrium of tumor cell types. Both our experimental data and mathematical modeling demonstrated that the complexity of tumor heterogeneity could be enhanced by the existence of chromosomes with structural abnormality, in addition to their mis-segregations. Overall, our findings show, for the first time, the involvement of chromosome instability in maintaining tumor heterogeneity, which underlies the enhanced growth, persistence and treatment resistance of cancers.     

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.     

Flow cytometric cell cycle analysis of muscle precursor cells cultured within 3D scaffolds in a perfusion bioreactor

It has been widely demonstrated that perfusion bioreactors improve in vitro three‐dimensional (3D) cultures in terms of high cell density and uniformity of cell distribution; however, the studies reported in literature were primarily based on qualitative analysis (histology, immunofluorescent staining) or on quantitative data averaged on the whole population (DNA assay, PCR). Studies on the behavior, in terms of cell cycle, of a cell population growing in 3D scaffolds in static or dynamic conditions are still absent. In this work, a perfusion bioreactor suitable to culture C₂C₁₂ muscle precursor cells within 3D porous collagen scaffolds was designed and developed and a method based on flowcytometric analyses for analyzing the cell cycle in the cell population was established. Cells were extracted by enzymatic digestion of the collagen scaffolds after 4, 7, and 10 days of culture, and flow cytometric live/dead and cell cycle analyses were performed with Propidium Iodide. A live/dead assay was used for validating the method for cell extraction and staining. Moreover, to investigate spatial heterogeneity of the cell population under perfusion conditions, two stacked scaffolds in the 3D domain, of which only the upstream layer was seeded, were analyzed separately. All results were compared with those obtained from static 3D cultures. The live/dead assay revealed the presence of less than 20% of dead cells, which did not affect the cell cycle analysis. Cell cycle analyses highlighted the increment of cell fractions in proliferating phases (S/G₂/M) owing to medium perfusion in long‐term cultures. After 7–10 days, the percentage of proliferating cells was 8–12% for dynamic cultures and 3–5% for the static controls. A higher fraction of proliferating cells was detected in the downstream scaffold. From a general perspective, this method provided data with a small standard deviation and detected the differences between static and dynamic cultures and between upper and lower scaffolds. Our methodology can be extended to other cell types to investigate the influence of 3D culture conditions on the expression of other relevant cell markers. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Single Cell Analysis of a Bacterial Sender-Receiver System

Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from Aliivibrio fischeri on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as ‘bacterial sensors’ for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their ‘sending power’ is determined.     

Two subpopulations of differentiated chondrocytes identified with a monoclonal antibody to keratan sulfate

We have prepared a monoclonal antibody, named MZ15, that specifically binds keratan sulfate. Immunofluorescence studies showed that the distribution of keratan sulfate in articular cartilage was not uniform: the amount of keratan sulfate increased with distance from the articular surface. Two subpopulations of chondrocytes could be distinguished after isolation from cartilage by the presence or absence of cell surface keratan sulfate. Keratan sulfate-negative chondrocytes were shown to come from the upper cartilage layers. There was therefore a direct correlation between biochemical heterogeneity of cartilage matrix and heterogeneity within the chondrocyte population. During growth in monolayer culture, superficial chondrocytes began to synthesize keratan sulfate, but the cells could still be distinguished from cultures of deep or unfractionated chondrocytes by their reduced substrate adhesiveness and tendency to remain rounded.     

Mitochondrial membrane potential selects hybridomas yielding high viability in fed-batch cultures

Prior research (Follstad, B. D.; Wang, D. I. C.; Stephanopoulos, G. Mitochondrial membrane potential differentiates cells resistant to apoptosis in hybridoma cultures. Eur. J. Biochem. 2000, 267, 6534-6540.) identified mitochondrial membrane potential (MMP) as a marker of hybridoma subpopulations resistant to apoptosis caused by a variety of apoptosis inducers. In this study, we investigated the viability of hybridoma cell cultures inoculated with cells of varying MMP in regular fed-batch operation. A hybridoma cell population was separated using FACS into subpopulations based on their mean mitochondrial membrane potential (MMP) as measured using the common mitochondrial stain, Rhodamine 123 (Rh123). These subpopulations showed dramatic differences in their apoptotic death kinetics. Fed-batches inoculated with a high MMP subpopulation reached higher viable cell concentrations and viabilities that were maintained for prolonged periods of time relative to fed-batches inoculated with low MMP subpopulations. These results underline the heterogeneous nature of hybridoma cell cultures and suggest that mitochondrial physiology is a critical parameter determining culture performance.     

Development of a mathematical model for evaluating the dynamics of normal and apoptotic Chinese hamster ovary cells

A metabolic flux based methodology was developed for modeling the metabolism of a Chinese hamster ovary cell line. The elimination of insignificant fluxes resulted in a simplified metabolic network which was the basis for modeling the significant metabolites. Employing kinetic rate expressions for growing and non-growing subpopulations, a logistic model was developed for cell growth and dynamic models were formulated to describe culture composition and monoclonal antibody (MAb) secretion. The model was validated for a range of nutrient concentrations. Good agreement was obtained between model predictions and experimental data. The ultimate goal of this study is to establish a comprehensive dynamic model which may be used for model-based optimization of the cell culture for MAb production in both batch and fed-batch systems.     

Physiological heterogeneity of Pseudomonas taetrolens during lactobionic acid production

Physiological heterogeneity constitutes a critical parameter in biotechnological systems since both metabolite yield and productivity are often hampered by the presence of undesired physiological cell subpopulations. In the present study, the physiological status and functionality of Pseudomonas taetrolens cells were monitored by multiparameter flow cytometry during fermentative lactobionic acid production at the shake-flask and bioreactor scale. In shake-flask fermentation, the onset of the lactobionic acid production phase was accompanied by a progressive loss of cellular metabolic activity, membrane polarization, and membrane integrity concomitantly to acidification. In fact, population dynamics has shown the prevalence of damaged and dead subpopulations when submitted to a pH?<?4 from 16?h onwards. Furthermore, fluorescence-activated cell sorting revealed that these sublethally injured cells were nonculturable. In contrast, P. taetrolens cells exhibited a robust physiological status during bioreactor cultivations performed with a pH-shifted strategy at 6.5, remaining predominantly healthy and metabolically active (>96?%) as well as maintaining bioconversion efficiency throughout the course of the fermentation. Additionally, an assessment of the seed culture’s physiological robustness was carried out in order to determine the best seed culture age. Results showed that bioreactor culture performance, growth, and lactobionic acid production efficiency were strongly dependent on the physiological heterogeneity displayed by the seed culture. This study provides the most suitable criteria for optimizing lactobionic acid production efficiency through a novel flow cytometric-based approach based on the physiological status of P. taetrolens. It also constitutes a valuable, broad-ranging methodology for the enhancement of microbial bioprocesses involved in the production of secondary metabolites.     

Stochastic differential equation model for cerebellar granule cell excitability

Neurons in the brain express intrinsic dynamic behavior which is known to be stochastic in nature. A crucial question in building models of neuronal excitability is how to be able to mimic the dynamic behavior of the biological counterpart accurately and how to perform simulations in the fastest possible way. The well-established Hodgkin-Huxley formalism has formed to a large extent the basis for building biophysically and anatomically detailed models of neurons. However, the deterministic Hodgkin-Huxley formalism does not take into account the stochastic behavior of voltage-dependent ion channels. Ion channel stochasticity is shown to be important in adjusting the transmembrane voltage dynamics at or close to the threshold of action potential firing, at the very least in small neurons. In order to achieve a better understanding of the dynamic behavior of a neuron, a new modeling and simulation approach based on stochastic differential equations and Brownian motion is developed. The basis of the work is a deterministic one-compartmental multi-conductance model of the cerebellar granule cell. This model includes six different types of voltage-dependent conductances described by Hodgkin-Huxley formalism and simple calcium dynamics. A new model for the granule cell is developed by incorporating stochasticity inherently present in the ion channel function into the gating variables of conductances. With the new stochastic model, the irregular electrophysiological activity of an in vitro granule cell is reproduced accurately, with the same parameter values for which the membrane potential of the original deterministic model exhibits regular behavior. The irregular electrophysiological activity includes experimentally observed random subthreshold oscillations, occasional spontaneous spikes, and clusters of action potentials. As a conclusion, the new stochastic differential equation model of the cerebellar granule cell excitability is found to expand the range of dynamics in comparison to the original deterministic model. Inclusion of stochastic elements in the operation of voltage-dependent conductances should thus be emphasized more in modeling the dynamic behavior of small neurons. Furthermore, the presented approach is valuable in providing faster computation times compared to the Markov chain type of modeling approaches and more sophisticated theoretical analysis tools compared to previously presented stochastic modeling approaches.     

Cell cycle- and age-dependent activation of Sod1p drives the formation of stress resistant cell subpopulations within clonal yeast cultures

Phenotypic heterogeneity describes non-genetic variation that exists between individual cells within isogenic populations. The basis for such heterogeneity is not well understood, but it is evident in a wide range of cellular functions and phenotypes and may be fundamental to the fitness of microorganisms. Here we use a suite of novel assays applied to yeast, to provide an explanation for the classic example of heterogeneous resistance to stress (copper). Cell cycle stage and replicative cell age, but not mitochondrial content, were found to be principal parameters underpinning differential Cu resistance: cell cycle-synchronized cells had relatively uniform Cu resistances, and replicative cell-age profiles differed markedly in sorted Cu-resistant and Cu-sensitive subpopulations. From a range of potential Cu-sensitive mutants, cup1Delta cells lacking Cu-metallothionein, and particularly sod1Delta cells lacking Cu, Zn-superoxide dismutase, exhibited diminished heterogeneity. Furthermore, age-dependent Cu resistance was largely abolished in cup1Delta and sod1Delta cells, whereas cell cycle-dependent Cu resistance was suppressed in sod1Delta cells. Sod1p activity oscillated approximately fivefold during the cell cycle, with peak activity coinciding with peak Cu-resistance. Thus, phenotypic heterogeneity in copper resistance is not stochastic but is driven by the progression of individual cells through the cell cycle and ageing, and is primarily dependent on only Sod1p, out of several gene products that can influence the averaged phenotype. We propose that such heterogeneity provides an important insurance mechanism for organisms; creating subpopulations that are pre-equipped for varied activities as needs may arise (e.g. when faced with stress), but without the permanent metabolic costs of constitutive expression.     

The Malignant Pleural Effusion as a Model to Investigate Intratumoral Heterogeneity in Lung Cancer

Malignant Pleural Effusions (MPE) may be useful as a model to study hierarchical progression of cancer and/or intratumoral heterogeneity. To strengthen the rationale for developing the MPE-model for these purposes, we set out to find evidence for the presence of cancer stem cells (CSC) in MPE and demonstrate an ability to sustain intratumoral heterogeneity in MPE-primary cultures. Our studies show that candidate lung CSC-expression signatures (PTEN, OCT4, hTERT, Bmi1, EZH2 and SUZ12) are evident in cell pellets isolated from MPE, and MPE-cytopathology also labels candidate-CSC (CD44, cMET, MDR-1, ALDH) subpopulations. Moreover, in primary cultures that use MPE as the source of both tumor cells and the tumor microenvironment (TME), candidate CSC are maintained over time. This allows us to live-sort candidate CSC-fractions from the MPE-tumor mix on the basis of surface markers (CD44, c-MET, uPAR, MDR-1) or differences in xenobiotic metabolism (ALDH). Thus, MPE-primary cultures provide an avenue to extract candidate CSC populations from individual (isogenic) MPE-tumors. This will allow us to test whether these cells can be discriminated in functional bioassays. Tumor heterogeneity in MPE-primary cultures is evidenced by variable immunolabeling, differences in colony-morphology, and differences in proliferation rates of cell subpopulations. Collectively, these data justify the ongoing development of the MPE-model for the investigation of intratumoral heterogeneity, tumor-TME interactions, and phenotypic validation of candidate lung CSC, in addition to providing direction for the pre-clinical development of rational therapeutics.